96 resultados para The healthy lifestyle


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Objectives. To confirm the association of a functional single-nucleotide polymorphism (SNP), C1858T (rs2476601), in the PTPN22 gene of British Caucasian rheumatoid arthritis (RA) patients and to evaluate its influence on the RA phenotype. Methods. A total of 686 RA patients and 566 healthy volunteers, all of British Caucasian origin, were genotyped for C1858T polymorphism by PCR-restriction fragment length polymorphism assay. Data were analysed using SPSS software and the χ 2 test as applicable. Results. The PTPN22 1858T risk allele was more prevalent in the RA patients (13.9%) compared with the healthy controls (10.3%) (P = 0.008, odds ratio 1.4, 95% confidence interval 1.09-1.79). The association of the T allele was restricted to those with rheumatoid factor (RF)-positive disease (n = 524, 76.4%) (P = 0.004, odds ratio 1.5, 95% confidence interval 1.1-1.9). We found no association between PTPN22 and the presence of the HLA-DRB1 shared epitope or clinical characteristics. Conclusions. We confirmed the previously reported association of PTPN22 with RF-positive RA, which was independent from the HLA-DRB1 genotype.

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Perfluoroalkyl acids (PFAAs) are a group of common chemicals that ubiquitously exist in wildlife and humans. Experimental data suggest that they may alter T-lymphocyte functioning in situ by preferentially enhancing the development of T-helper 2 (TH2)- and inhibiting TH1-lymphocyte development and might increase allergic inflammation, but few human studies have been conducted. To evaluate the association between serum PFAAs concentrations and T-lymphocyte-related immunological markers of asthma in children, and further to assess whether gender modified this association, 231 asthmatic children and 225 non-asthmatic control children from Northern Taiwan were recruited into the Genetic and Biomarker study for Childhood Asthma. Serum concentrations of ten PFAAs and levels of TH1 [interferon (IFN)-γ, interleukin (IL)-2] and TH2 (IL-4 and IL-5) cytokines were measured. The results showed that asthmatics had significantly higher serum PFAAs concentrations compared with the healthy controls. When stratified by gender, a greater number of significant associations between PFAAs and asthma outcomeswere found in males than in females. Among males, adjusted odds ratios for asthma among those with the highest versus lowest quartile of PFAAs exposure ranged from 2.59 (95% CI: 1.14, 5.87) for the perfluorobutanesulfonate (PFBS) to 4.38 (95% CI: 2.02, 9.50) for perfluorooctanesulfonate (PFOS); and serum PFAAs were associated positively with TH2 cytokines and inversely with TH1 cytokines among male asthmatics. Among females, no significant associations between PFAAs and TH2 cytokines could be detected. In conclusion, increased serum PFAAs levels may promote TH cell dysregulation and alter the availability of key TH1 and TH2 cytokines, ultimately contributing to the development of asthma that may differentially impact males to a greater degree than females. These results have potential relevance in asthma prevention.

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Objective: To examine the extent to which socio-demographics, modifiable lifestyle, and physical health status influence the mental health of post-menopausal Australian women. Methods: Cross-sectional data on health status, chronic disease and modifiable lifestyle factors were collected from a random cross-section of 340 women aged 60-70 years, residing in Queensland, Australia. Structural equation modelling (SEM) was used to measure the effect of a range of socio-demographic characteristics, modifiable lifestyle factors, and health markers (self-reported physical health, history of chronic illness) on the latent construct of mental health status. Mental health was evaluated using the Medical Outcomes Study Short Form 12 (SF-12®) which examined and Center for Epidemiologic Studies Depression Scale (CES-D). Results: The model was a good fit for the data (χ2=4.582, df=3, p=0.205) suggesting that mental health is negatively correlated with sleep disturbance (β = -0.612, p <0.001), and a history of depression (β = -0.141, p = 0.024).While mental health was associated with poor sleep, it was not correlated with most lifestyle factors (BMI, alcohol consumption, or cigarette smoking) or socio-demographics like age, income or employment category and they were removed from the final model. Conclusion: Research suggests that it is important to engage in a range of health promoting behaviours to preserve good health. We found that predictors of current mental health status included sleep disturbance, and past mental health problems, while socio-demographics and modifiable lifestyle had little impact. It may be however, that these factors influenced other variables associated with the mental health of post-menopausal women, and these relationships warrant further investigation.

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BACKGROUND: Despite advancements in our understanding of the importance of stress reduction in achieving good health, we still only have limited insight into the impact of stress on cellular function. Recent studies have suggested that exposure to prolonged psychological stress may alter an individual's physiological responses, and contribute to morbidity and mortality. This paper presents an overview of the study protocol we are using to examine the impact of life stressors on lifestyle factors, health-related quality of life and novel and established biomarkers of stress in midlife and older Australian women.The primary aim of this study is to explore the links between chronic psychological stress on both subjective and objective health markers in midlife and older Australian women. The study examines the extent to which exposure frightening, upsetting or stressful events such as natural disasters, illness or death of a relative, miscarriage and relationship conflict is correlated with a variety of objective and subjective health markers.Methods/design: This study is embedded within the longitudinal Healthy Aging of Women's study which has collected data from midlife and older Australian women at 5 yearly intervals since 2001, and uses the Allostastic model of women's health by Groer and colleagues in 2010. The current study expands the focus of the HOW study and will assess the impact of life stressors on quality of life and clinical biomarkers in midlife and older Australian women to explain the impact of chronic psychological stress in women. DISCUSSION: The proposed study hypothesizes that women are at increased risk of exposure to multiple or repeated stressors, some being unique to women, and the frequency and chronicity of stressors increases women's risk of adverse health outcomes. This study aims to further our understanding of the relationships between stressful life experiences, perceived quality of life, stress biomarkers, chronic illness, and health status in women.

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Purpose: To examine the extent to which socio-demographic characteristics, modifiable lifestyle factors and health status influence the mental health of midlife and older Australian women from the Australian Healthy Aging of Women (HOW) study. Methods: Data on health status, chronic disease and modifiable lifestyle factors were collected from a random sample of 340 women aged 40-65 years, residing in Queensland, Australia in 2011. Structural equation modelling (SEM) was used to measure the effect of a range of socio-demographic characteristics (marital status, age, income), modifiable lifestyle factors (caffeine intake, alcohol consumption, exercise, physical activity, sleep), and health markers (self-reported physical health, history of chronic illness) on the latent construct, mental health. Mental health was evaluated using the Medical Outcomes Study Short Form 12 (SF-12®) and the Center for Epidemiologic Studies Depression Scale (CES-D). Results: The model was a good fit for the data (χ2 = 40.166, df =312, p 0.125, CFI = 0.976, TLI = 0.950, RMSEA = 0.030, 90% CI = 0.000-0.053); the model suggested mental health was negatively influenced by sleep disturbance (β = -0.628), sedentary lifestyle (β = -0.137), having been diagnosed with one or more chronic illnesses (β = -0.203), and poor self-reported physical health (β = - 0.161). While mental health was associated with sleep, it was not correlated with many other lifestyle factors (BMI (β = -0.050), alcohol consumption (β = 0.079), or cigarette smoking (β = 0.008)) or background socio-demographic characteristics (age (β = 0.078), or income (β = -0.039)). Conclusion: While research suggests that it is important to engage in a range health promoting behaviours to preserve good health, we found that only sleep disturbance, physical health, chronic illness and level of physical activity predicted current mental health. However, while socio-demographic characteristics and modifiable lifestyle factors seemed to have little direct impact on mental health, they probably had an indirect effect.

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Objectives: Previous research has linked unhealthy lifestyle with a range of negative health outcomes in women. As women age however, they may have fewer performance expectations, but may view their health more positively. Clearly, the experiences of midlife and older women in relation to health and wellbeing need further exploration. The purpose of this study is to examine the factors associated with poor health-related quality of life in midlife (HRQoL) and older Australian women. Methods: The Australian longitudinal Healthy Aging of Women (HOW) study prospectively examines HRQoL, chronic disease and modifiable lifestyle factors midlife and older women as they age. Random sampling was used to select rural and urban based women from South-East Queensland, Australia. Data were collected from 386 women at three time points over the last decade (2001, 2004 and 2011). Results: The average age of women in this study was 65 years (SD = 2.82). Almost three-quarters (73%, n = 248) of the sample were married or living as though married, nine per cent (n = 30) were separated or divorced and a small proportion were had never married (n = 13). Most (86%, n = 291) of the women sample reported being Australian born, around one quarter (34%, n = 114) had completed additional study since leaving school (university degree or diploma). Over half (55%, n = 186) of participants were retired, one quarter (25%, n = 85) were in paid employment and the remained were unemployed (1%, n = 4), unable to work because of illness (2%, n = 6) or worked within the home (17%, n = 56). Using data collected over time we examined the relationship between a range of modifiable lifestyle factors and mental health using structural equation modelling. The overall model exhibited a good fit with the data. Poor sleep quality was associated with reduced mental health while better mental health was reported in women who exercised regularly and satisfied with their currently weight. As hypothesized, past mental health was a significant mediator of current mental health. Conclusions: These findings demonstrate that the mental health of women is complex and needs to be understood not only in terms of current lifestyle but also in relation to previously reported health status.